Indoor Positioning and Navigation
Tomažič, Simon (editor)
In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot.
Keywordsdynamic objects identification and localization; laser cluster; radial velocity similarity; Pearson correlation coefficient; particle filter; trilateral indoor positioning; RSSI filter; RSSI classification; stability; accuracy; inertial navigation system; artificial neural network; motion tracking; sensor fusion; indoor navigation system; indoor positioning; indoor navigation; radiating cable; leaky feeder; augmented reality; Bluetooth; indoor positioning system; smart hospital; indoor; positioning; visually impaired; deep learning; multi-layered perceptron; inertial sensor; smartphone; multi-variational message passing (M-VMP); factor graph (FG); second-order Taylor expansion; cooperative localization; joint estimation of position and clock; RTLS; indoor positioning system (IPS); position data; industry 4.0; traceability; product tracking; fingerprinting localization; Bluetooth low energy; Wi-Fi; performance metrics; positioning algorithms; location source optimization; fuzzy comprehensive evaluation; DCPCRLB; UAV; unmanned aerial vehicles; NWPS; indoor positioning systems; GPS denied; GNSS denied; autonomous vehicles; visible light positioning; mobile robot; calibration; appearance-based localization; computer vision; Gaussian processes; manifold learning; robot vision systems; image manifold; descriptor manifold; indoor fingerprinting localization; Gaussian filter; Kalman filter; received signal strength indicator; channel state information; indoor localization; visual-inertial SLAM; constrained optimization; path loss model; particle swarm optimization; beacon; absolute position system; cooperative algorithm; intercepting vehicles; robot framework; UWB sensors; Internet of Things (IoT); wireless sensor network (WSN); switched-beam antenna; electronically steerable parasitic array radiator (ESPAR) antenna; received signal strength (RSS); fingerprinting; down-conversion; GPS; navigation; RF repeaters; up-conversion; n/a
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Publication date and placeBasel, Switzerland, 2021
Technology: general issues
Energy industries & utilities